Probabilistic and Possibilistic Networks and How To Learn Them from Data

In this paper we explain in a tutorial manner the technique of reasoning in probabilistic and possibilistic network structures, which is based on the idea to decompose a multi-dimensional probability or possibility distribution and to draw inferences using only the parts of the decomposition. Since constructing probabilistic and possibilistic networks by hand can be tedious and time-consuming, we also discuss how ta learn probabilistic and possibilistic networks from a data, i.e. how to determine from a database of sample cases an appropriate decomposition of the underlying probability or possibility distribution.